Instructions to use Pinwheel/wav2vec2-base-timit-demo-colab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pinwheel/wav2vec2-base-timit-demo-colab with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Pinwheel/wav2vec2-base-timit-demo-colab")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Pinwheel/wav2vec2-base-timit-demo-colab") model = AutoModelForCTC.from_pretrained("Pinwheel/wav2vec2-base-timit-demo-colab", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88d6f668f3681cbe7f4d1cd5e2799d9ea9c1b1aa65acf97cf4d2e6765d00c17d
- Size of remote file:
- 378 MB
- SHA256:
- f390b83a6f2e50bb00a86bd72f197291aca831dd73cf55d8f93820ae2eab3091
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.